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by staticroostermedia-arch
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Rust
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mcp_fibered_linguistic_equivalence

Evaluate equivalence between two linguistic presentations (e.g., syntactic vs semantic) using VSA geometric/cosine metrics, returning a CRS-scored equivalence block.

Instructions

Phase 3: Fibered equivalence check between two Linguistic* presentations (syntactic vs semantic etc). Returns CRS-scored equivalence block via VSA geometric/cosine on phase reps.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "mcp_fibered_linguistic_equivalence"
  }
}

Input Schema

NameRequiredDescriptionDefault
bundle_aNoFirst LinguisticDiscourseBundle
bundle_bNoSecond LinguisticDiscourseBundle

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.0-beta.2

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description should disclose behavioral traits. It mentions 'Phase 3' and the computation method (VSA geometric/cosine) but omits side effects, authorization needs, rate limits, or whether it modifies state. The description is too abstract for an agent to understand operational impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise (two sentences) and front-loads the core purpose. However, it relies heavily on jargon ('Fibered', 'Linguistic*', 'CRS-scored', 'VSA'), reducing accessibility for a general AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool involves complex domain concepts and nested object parameters, but the description does not explain what a LinguisticDiscourseBundle is, how to prepare it, or interpret the output. No output schema exists to fill the gap. Incomplete for informed use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions provide minimal labels ('First LinguisticDiscourseBundle'), and the description adds no further explanation of what constitutes a valid bundle or how to obtain one. With 100% schema coverage but shallow descriptions, the description fails to compensate for the lack of semantic depth.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action: 'fibered equivalence check' and identifies the resources ('two Linguistic* presentations'). It mentions the output ('CRS-scored equivalence block'), providing a clear purpose for domain-aware agents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like mcp_linguistic_calculus or other engram tools. No prerequisites or context for invocation are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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